An improved calibration and uncertainty analysis approach using a multicriteria sequential algorithm for hydrological modeling
Bibliographic record
Abstract
Hydrological models are widely used as simplified, conceptual, mathematical representatives for water resource management. The performance of hydrological modeling is usually challenged by model calibration and uncertainty analysis during modeling exercises. In this study, a multicriteria sequential calibration and uncertainty analysis (MS-CUA) method was proposed to improve the efficiency and performance of hydrological modeling with high reliability. To evaluate the performance and feasibility of the proposed method, two case studies were conducted in comparison with two other methods, sequential uncertainty fitting algorithm (SUFI-2) and generalized likelihood uncertainty estimation (GLUE). The results indicated that the MS-CUA method could quickly locate the highest posterior density regions to improve computational efficiency. The developed method also provided better-calibrated results (e.g., the higher NSE value of 0.91, 0.97, and 0.74) and more balanced uncertainty analysis results (e.g., the largest P/R ratio values of 1.23, 2.15, and 1.00) comparing with other traditional methods for both case studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".